Denoising Method for Echocardiographic Images Based on the Second Generation Curvelet Transform

As a novel multi-scale analytical technique, curvelet transform is developed to propose a better sparse representation of images with singularities along curves. Speckle noise is a natural property in medical ultrasound images, which often result in blurred image features. Therefore, speckle noise reduction is a significant and delicate research subject in medical images processing. In this paper, a new denoising approach for echocardiographic images corrupted with speckle noise is presented, after introducing the basic theory of curvelet transform. In addition, in order to suppress the curvelet-like artifacts, the conventional shrinkage results can be further processed by total variation diffusion. The experimental results demonstrate that, in comparison to some existing techniques, the suggested method gives better effect in speckle removing while preserving the edges of the echocardiographic images in terms of quantitative performance as well as in visual quality of the images. The application of curvelet transform reveals its great potential in echocardiographic images processing.